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Josh Starmer
In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset.
Read more
In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset.
Read more
In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.
Prerequisites:
In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices.
Notes:
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/1xf7zw/classification
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What's inside
Syllabus
Classification Trees in Python, from Start To Finish
In this lesson we will use scikit-learn and Cost Complexity Pruning to build a Classification Tree, which uses continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops foundational Decision Trees skills, which are highly relevant to AI and Data Science
Builds foundation for more advanced courses and training in AI and Data Science
Teaches continuous and categorical data handling, which is standard in industry
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Reviews summary
Well-liked classification tree course
Learners say that this well-received Classification Trees in Python, From Start To Finish course is good for beginners and is very engaging. They praise the instructor, Josh Starmer, for his clear explanations, easy-to-follow videos, and engaging teaching style. While some learners found the Rhyme platform to be difficult, most believe the content provided was more than enough to overcome these issues.
Learners mention that the practical project helps them to apply their new skills to actual data.
"Very good and clear project, ideal to imporve knowledge in supervised learning and decision trees."
"Really awesome !!! Understood using Classification tress end to end in one go."
Many learners say that this course includes helpful and detailed explanations of classification trees and techniques.
"The instructor does a wonderful job of explaining concepts and providing useful code."
"All the code and concepts were clearly explained."
Josh Starmer is widely known for his engaging and entertaining teaching style.
"Josh Starmer's videos and courses are always simple and easy to understand."
"The instructor has a great teaching style."
Some learners believe that the course is overpriced for the content provided.
"Not sure I would pay U$ 10 for simple projects when there are similar excellent code freely available on Kaggle or github"
Many learners found the Rhyme platform to be problematic, citing issues with video playback and the user interface.
"Rhyme is such a shit tool"
"It is really bad because we cannot maximize windows"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Classification Trees in Python, From Start To Finish with these
activities:
Brush Up on Python Basics
Show steps
Ensure a strong foundation in Python syntax and data structures to support your learning in this course.
Browse courses on
Python
Show steps
-
Review basic Python concepts, such as variables, data types, and control flow
-
Practice writing simple Python scripts and functions
Read 'Python Machine Learning' by Sebastian Raschka
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques, including classification trees, in the context of Python.
Show steps
-
Read the chapters on classification trees, focusing on the theory and implementation in Python
-
Work through the exercises and examples provided in the book
Compile Resources on Classification Trees
Show steps
Gather and organize valuable resources related to classification trees, providing easy access to additional learning materials.
Browse courses on
Classification Trees
Show steps
-
Search for and identify reputable articles, tutorials, and online courses on classification trees
-
Create a central location, such as a digital folder or online document, to store the resources
-
Categorize and organize the resources for easy retrieval
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Coding Challenges on Classification Trees
Show steps
Challenge yourself with coding problems related to classification trees, solidifying your understanding of their implementation and applications.
Browse courses on
Classification Trees
Show steps
-
Find online coding platforms or challenges that offer classification tree problems
-
Attempt to solve the problems using Python, focusing on efficient and accurate solutions
-
Review your solutions and identify areas for improvement
Join a Study Group or Discussion Forum
Show steps
Engage with peers to discuss concepts, share insights, and reinforce your understanding of classification trees and Python.
Browse courses on
Classification Trees
Show steps
-
Identify or join an online or in-person study group or discussion forum
-
Regularly participate in discussions, ask questions, and share your knowledge
Follow Tutorials on Advanced Classification Tree Techniques
Show steps
Explore advanced classification tree techniques, such as ensemble methods, to improve the accuracy and robustness of your models.
Browse courses on
Classification Trees
Show steps
-
Identify online tutorials or courses covering advanced classification tree techniques
-
Follow the tutorials step-by-step, implementing the techniques in Python
-
Apply the learned techniques to your own classification tree model and evaluate the improvements
Create a Classification Tree Model from Scratch
Show steps
Build a classification tree from scratch to reinforce your understanding of the process and algorithms involved.
Browse courses on
Classification Trees
Show steps
-
Gather a dataset with categorical and continuous values
-
Write code to pre-process the data, including handling missing values and encoding categorical data
-
Implement the Classification Tree algorithm using Python, including splitting criteria, node construction, and pruning
Design a Dashboard to Visualize Classification Tree Results
Show steps
Create a visually appealing dashboard to present the results of your classification tree model, making it easier to interpret and communicate.
Browse courses on
Data Visualization
Show steps
-
Choose a data visualization tool or library, such as Plotly or Seaborn
-
Design the layout of the dashboard, including graphs, charts, and other visualizations
-
Develop code to connect the dashboard to your classification tree model and display the results
Brush Up on Python Basics
Show steps
Ensure a strong foundation in Python syntax and data structures to support your learning in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic Python concepts, such as variables, data types, and control flow
- Practice writing simple Python scripts and functions
Read 'Python Machine Learning' by Sebastian Raschka
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques, including classification trees, in the context of Python.
Show steps
Show steps
Show steps
- Read the chapters on classification trees, focusing on the theory and implementation in Python
- Work through the exercises and examples provided in the book
Compile Resources on Classification Trees
Show steps
Gather and organize valuable resources related to classification trees, providing easy access to additional learning materials.
Browse courses on
Classification Trees
Show steps
Show steps
Show steps
- Search for and identify reputable articles, tutorials, and online courses on classification trees
- Create a central location, such as a digital folder or online document, to store the resources
- Categorize and organize the resources for easy retrieval
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Coding Challenges on Classification Trees
Show steps
Challenge yourself with coding problems related to classification trees, solidifying your understanding of their implementation and applications.
Browse courses on
Classification Trees
Show steps
Show steps
Show steps
- Find online coding platforms or challenges that offer classification tree problems
- Attempt to solve the problems using Python, focusing on efficient and accurate solutions
- Review your solutions and identify areas for improvement
Join a Study Group or Discussion Forum
Show steps
Engage with peers to discuss concepts, share insights, and reinforce your understanding of classification trees and Python.
Browse courses on
Classification Trees
Show steps
Show steps
Show steps
- Identify or join an online or in-person study group or discussion forum
- Regularly participate in discussions, ask questions, and share your knowledge
Follow Tutorials on Advanced Classification Tree Techniques
Show steps
Explore advanced classification tree techniques, such as ensemble methods, to improve the accuracy and robustness of your models.
Browse courses on
Classification Trees
Show steps
Show steps
Show steps
- Identify online tutorials or courses covering advanced classification tree techniques
- Follow the tutorials step-by-step, implementing the techniques in Python
- Apply the learned techniques to your own classification tree model and evaluate the improvements
Create a Classification Tree Model from Scratch
Show steps
Build a classification tree from scratch to reinforce your understanding of the process and algorithms involved.
Browse courses on
Classification Trees
Show steps
Show steps
Show steps
- Gather a dataset with categorical and continuous values
- Write code to pre-process the data, including handling missing values and encoding categorical data
- Implement the Classification Tree algorithm using Python, including splitting criteria, node construction, and pruning
Design a Dashboard to Visualize Classification Tree Results
Show steps
Create a visually appealing dashboard to present the results of your classification tree model, making it easier to interpret and communicate.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a data visualization tool or library, such as Plotly or Seaborn
- Design the layout of the dashboard, including graphs, charts, and other visualizations
- Develop code to connect the dashboard to your classification tree model and display the results
Career center
Learners who complete Classification Trees in Python, From Start To Finish will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are heavily reliant on classification trees to create and test predictive models. This course provides a solid foundation for building classification trees in Python, which is a crucial skill for Data Scientists. By understanding how to construct and optimize classification trees, learners can enhance their ability to analyze data, identify patterns, and make accurate predictions. Furthermore, this course covers topics such as cost complexity pruning and cross-validation, which are essential techniques for preventing overfitting and ensuring the robustness of models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers play a critical role in developing and deploying machine learning models. This course provides a comprehensive introduction to building classification trees, which are a fundamental type of machine learning model. Learners will gain hands-on experience in implementing classification trees in Python, including techniques for handling missing data and categorical variables. By mastering these skills, learners can contribute effectively to the design, implementation, and maintenance of machine learning systems.
Data Analyst
Data Analyst
Data Analysts leverage classification trees to uncover insights from data and make informed decisions. This course equips learners with the practical skills to build and interpret classification trees in Python. By learning how to prepare data, construct models, and evaluate their performance, learners can enhance their ability to analyze data, identify trends, and communicate findings effectively.
Statistician
Statistician
Statisticians often utilize classification trees to analyze complex data and make predictions. This course provides a practical foundation for building classification trees in Python, covering topics such as data preprocessing, model selection, and performance evaluation. By mastering these skills, learners can strengthen their ability to conduct statistical analyses, interpret results, and draw meaningful conclusions from data.
Business Analyst
Business Analyst
Business Analysts leverage data analysis to drive informed decision-making. This course provides a valuable introduction to building classification trees in Python, a technique commonly used for data classification and prediction. Learners will gain hands-on experience in preparing data, constructing models, and interpreting results. By mastering these skills, learners can enhance their ability to analyze business data, identify opportunities, and make data-driven recommendations.
Research Analyst
Research Analyst
Research Analysts utilize classification trees to analyze data, identify patterns, and make predictions. This course provides a comprehensive introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to conduct research, analyze data, and draw meaningful conclusions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts rely on classification trees to build predictive models and analyze financial data. This course provides a solid foundation for building classification trees in Python, covering techniques for handling missing data, transforming categorical variables, and optimizing model performance. By mastering these skills, learners can enhance their ability to develop and evaluate quantitative models, identify investment opportunities, and make informed financial decisions.
Risk Analyst
Risk Analyst
Risk Analysts leverage classification trees to assess and manage risk. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify risks, and develop mitigation strategies.
Actuary
Actuary
Actuaries utilize classification trees to analyze data and make predictions in the insurance industry. This course provides a solid foundation for building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, assess risk, and develop insurance products and pricing strategies.
Software Engineer
Software Engineer
Software Engineers may encounter classification trees in the context of data analysis and machine learning tasks. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, develop data-driven applications, and make informed decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts utilize classification trees to solve complex decision-making problems. This course provides a comprehensive introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, develop optimization models, and make informed decisions.
Financial Analyst
Financial Analyst
Financial Analysts may leverage classification trees to analyze financial data and make predictions. This course provides a solid foundation for building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify investment opportunities, and make informed financial decisions.
Data Engineer
Data Engineer
Data Engineers may encounter classification trees in the context of data analysis and machine learning tasks. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, develop data pipelines, and ensure the integrity and quality of data.
Product Manager
Product Manager
Product Managers may leverage classification trees to analyze data and make decisions about product development. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify customer needs, and develop successful products.
Business Development Manager
Business Development Manager
Business Development Managers may leverage classification trees to analyze data and identify sales opportunities. This course provides a solid foundation for building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify potential customers, and develop effective sales strategies.
For more career information including salaries, visit:
OpenCourser.com/course/1xf7zw/classification
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Classification Trees in Python, From Start To Finish.
Comprehensive reference on statistical learning methods, including decision trees. It provides a theoretical and practical understanding of these algorithms.
Provides a practical guide to building machine learning models in Python using popular libraries like scikit-learn and TensorFlow. It covers decision trees and other classification algorithms in-depth.
Provides a comprehensive overview of machine learning algorithms and techniques in Python, including decision trees.
Provides a comprehensive overview of machine learning algorithms and techniques in Python, including decision trees.
Save
Provides a concise and comprehensive reference for Python programming. It good resource for learners who want to quickly find information on Python functions and modules.
Save
Provides a deep dive into Python programming best practices. It good resource for learners who want to improve their Python skills.
Provides a practical guide to building machine learning models in Python. It covers decision trees and other classification algorithms in a hands-on manner.
Provides a gentle introduction to machine learning concepts and algorithms in Python. It covers decision trees as a foundational topic.
Provides a practical guide to building deep learning models in Python using the fastai library. While it does not cover decision trees specifically, it provides a good foundation for understanding the underlying concepts.
Save
Provides a comprehensive introduction to Python programming. It covers basic concepts, data structures, and algorithms.
Provides a fast-paced introduction to Python programming. It good starting point for learners who want to quickly get up and running with Python.
Provides a practical guide to Python programming, covering basic concepts and data structures. It good starting point for learners who are new to Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1xf7zw/classification
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Josh Starmer
Language
English
Good to know
Develops foundational Decision Trees skills, which are highly relevant to AI and Data Science
Builds foundation for more advanced courses and training in AI and Data Science
Teaches continuous and categorical data handling, which is standard in industry
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